Search Results (828)

The deep integration of the Internet of Things (IoT) and blockchain technology enables emerging applications in multi-party collaboration and trusted data sharing. However, the scalability constraints of blockchain networks remain a major bottleneck when handling high-frequency interactions in IoT–blockchain systems. Sharding addresses this challenge by partitioning the blockchain network into parallel sub-networks. Nevertheless, it introduces significant coordination overhead for cross-shard transactions. Among mitigation strategies, Broker-based mechanisms (e.g., BrokerChain) have attracted increasing attention for their efficiency in handling cross-shard communication by reducing verification overhead and communication latency. Despite these advantages, existing research typically treats the Broker group as a fixed configuration, neglecting the impact of Broker selection on system performance. To bridge this gap, this paper proposes the Accumulative Activity–Temporal Liveness Broker Selection Strategy (AT-BSS) to optimize cross-shard transaction processing in sharded IoT–blockchains. Specifically, we formally characterize the Accumulative Activity and Temporal Liveness of accounts in the account–transaction network and use these two metrics to identify accounts that maximize transaction-aggregation efficiency. We implement AT-BSS on the BlockEmulator platform and evaluate it against two baselines, namely, ABChain and BrokerChain. Under different settings of the number of Brokers (BrokerNum), number of shards (ShardNum), transaction arrival rate (InjectSpeed), and maximum block size (MaxBlockSize), AT-BSS consistently outperforms both baselines in terms of Transactions Per Second (TPS), Transaction Confirmation Latency (TCL), and Cross-shard Transaction Ratio (CTX). Compared with ABChain, AT-BSS achieves up to 15.5% higher TPS and reduces TCL and CTX by up to 80.2% and 28.7%, respectively. AT-BSS yields more pronounced results over BrokerChain, with TPS improvements of up to 229% and reductions of up to 97.7% in TCL and 80.5% in CTX. Full article

The rapid growth of the Internet of Things (IoT) is fundamentally altering industrial and economic landscapes by embedding smart, connected devices into everyday operations. Despite these benefits, significant concerns regarding data protection and user privacy continue to obstruct the widespread use of these technologies, particularly with the looming threat of quantum computing. Implementing post-quantum cryptographic (PQC) solutions is vital for addressing these risks, yet the limited resources found in IoT edge devices present major deployment challenges. Lattice-based cryptography has become a leading solution to these problems, largely because it depends on efficient polynomial multiplication. Enhancing the execution of this mathematical operation is crucial for improving the overall performance of PQC protocols. In this work, we introduce a hybrid serial–parallel systolic architecture specifically engineered for polynomial multiplication within the Binary Ring Learning With Errors (BRLWE) scheme. Designed for the security processors used in IoT hardware, this architecture significantly increases processing speeds while minimizing the use of hardware resources and reducing energy consumption. Such improvements are critical for establishing a secure IoT infrastructure that is resilient against quantum-era attacks and capable of supporting industrial expansion. Moreover, this research aligns with global Sustainable Development Goals (SDGs) 8 and 9 by building trust in innovative systems and fostering a more secure, sustainable, and productive digital economy. Full article

Assistive mobility devices for small animals require reliable monitoring to ensure safe and comfortable operation without increasing system complexity or invasiveness. This study presents a low-cost monitoring platform that integrates a laser-induced graphene (LIG) contact-temperature sensor into a passive mobility device for small dogs, supported by a lightweight Internet of Things (IoT) architecture. The system combines contact temperature, ambient temperature, speed, and obstacle distance using an energy-aware acquisition strategy and prioritized wireless transmission for near-real-time monitoring. An unsupervised anomaly detection framework based on Isolation Forest identifies potentially unsafe operating conditions without labeled pathological data by leveraging absolute temperature and the differential feature $\Delta T$ between contact and ambient measurements. Experimental validation was conducted under controlled indoor conditions across six independent sessions with a small-breed dog, including static and dynamic phases to ensure repeatability. The system achieved packet delivery ratios of approximately 95%, with typical end-to-end latencies below 500 ms and worst-case delays below 850 ms. The proposed approach detected localized thermal deviations associated with friction or prolonged contact while remaining robust to normal activity- and environment-driven variations. These results demonstrate the feasibility of integrating LIG-based sensing and unsupervised analytics into assistive animal mobility platforms to enhance safety through continuous, non-invasive monitoring. Full article

Wind energy harvesting based on triboelectric nanogenerators (TENGs) is a promising solution for powering distributed Internet of Things (IoT) nodes, yet its practical efficiency and stability are often hindered by the fluctuating and unpredictable nature of wind. Here, we propose a self-regulating TENG (SR-TENG) that leverages the synergistic effects of centrifugal, elastic, and frictional forces to automatically switch between non-contact and contact modes based on wind speed. This configuration achieves an ultra-low start-up wind speed of 0.86 m/s, ensures sustainable high-performance output across a broad wind speed range, and exhibits excellent durability with no observable performance degradation during 23,000 s of continuous operation at 375 rpm. Systematic structural optimization enables the SR-TENG to reach a peak open-circuit voltage of 140 V, a short-circuit current of 12.5 μA, and a transferred charge of 300 nC at 375 rpm. When integrated with a customized power management circuit, the system delivers a 30.39-fold increase in effective output power at a 1 MΩ load and a 4-fold faster charging rate for a 10 μF capacitor. For practical validation, the harvested ambient wind energy successfully powers a wireless temperature-humidity sensor for real-time cloud data transmission. These results highlight that the SR-TENG holds great potential for advanced wind energy harvesting and self-powered sensing applications in distributed IoT systems. Full article

Energy harvesting plays a pivotal role in enabling sustainable power supply for the Internet of Things and distributed sensor networks, particularly for low-power devices. Piezoelectric energy harvesters based on vortex-induced vibrations offer a promising solution for low-wind-speed applications, yet their performance is constrained by limited bandwidth and sensitivity to wind speed variations. This study addresses these limitations by proposing a novel multi-parameter adjustable piezoelectric energy harvester featuring an inclined cylindrical bluff body. By systematically tuning the inclination angle and installation position, the device achieves substantial performance improvements. Experimental results indicate that the optimized configuration yields a wider operational frequency band and enhanced energy conversion efficiency. Through the experimental results, we discovered the existence of the double-peak phenomenon and the plateau phenomenon. The voltage value of the second peak can reach up to 122.4% of the maximum voltage of the first peak. The duration of the maximum plateau phase can maintain between the wind speed of 2.3 m/s and 5.7 m/s. Full article

When devices equipped with multiple wireless network interfaces access the Internet via Wi-Fi, 4G, and 5G, external factors such as radio interference can increase packet loss rates, resulting in reduced communication speed. To address this issue, two approaches exist: the use of Bottleneck Bandwidth and Round-trip propagation time (BBR), a congestion control algorithm designed to mitigate the impact of packet loss and bicasting in multihomed networks. Bicasting in multihomed networks exploits multiple network paths by transmitting identical packets simultaneously over different networks, thereby reducing effective packet loss and mitigating throughput reduction. In this paper, we introduce a novel network architecture that effectively operates in lossy networks by combining bicasting with BBR. By utilizing QUIC and OpenFlow, the proposed architecture enables the construction of a multihomed network that is independent of the operating system (OS), allowing flexible configuration of congestion control algorithms. Furthermore, the introduction of a QUIC proxy enables the use of existing server-side applications without requiring any modifications. Using the proposed multihomed network, we evaluate communication performance for unicasting and bicasting under varying packet loss rates, and we also analyze fairness with competing Transmission control protocol (TCP) flows. The results indicate that the combination of BBRv3 and bicasting achieves fivefold higher throughput than TCP unicasting at a 1% packet loss rate while preserving fairness with competing TCP flows. Full article

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter $\alpha$ to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings. Full article

Federated Learning (FL) represents a promising paradigm for collaborative model training across numerous devices, preserving data locality and offering potential privacy benefits for industries such as finance, healthcare, and Internet of Things (IoT). Nonetheless, real-world deployments of FL encounter challenges arising from dynamic and diverse environments, which adversely affect training speed and model convergence. To address these issues, this paper introduces FedDCS, an adaptive federated learning framework that effectively manages resources during training through two primary innovations. First, it establishes a reliable method for predicting client training durations, estimating completion times while filtering noise and detecting performance variations. Second, it implements a two-stage adaptive waiting strategy that dynamically determines the optimal timing and selection of client batches for aggregation, thereby balancing collection efficiency with model accuracy. This approach optimizes the trade-off between efficiency and accuracy in heterogeneous settings. Extensive evaluations on datasets such as Fashion-MNIST and CIFAR-10/100, incorporating simulated device and data heterogeneity, demonstrate that FedDCS consistently achieves superior time efficiency and higher global model accuracy compared to state-of-the-art (e.g., synchronous, asynchronous, and semi-asynchronous) baselines. Its robustness and versatility render it effective across various complex and heterogeneous environments. Full article

The global push to advance smart and digital forestry relies on emerging technologies to support efficient, AI-assisted, and data-driven forest management, but many forest operations occur in remote forests where reliable internet connectivity is unavailable. Low Earth Orbit (LEO) satellite constellations such as Starlink may provide reliable connectivity where cellular networks are unavailable. The performance of LEO-based solutions remains poorly understood under forest canopies, and empirical evaluations linking canopy characteristics to connectivity performance are largely lacking. In this study, the effect of forest vegetation on Starlink performance below the canopy was evaluated by placing a satellite receiver at thirty randomly selected permanent single tree inventory plots on the University of Idaho Experimental Forest and measuring connection success, connection time, and upload and download speeds along 50 m transects in all cardinal directions. LiDAR-derived stand density index (SDI), leaf area index (LAI), rumple index (RI), and vegetation cover (VC) were used to quantify canopy structure. Principal Component Analysis and survival analysis showed that higher values of PC1, primarily driven by SDI, LAI, and RI, reduced the probability of establishing a connection. Linear regression analysis indicated that higher SDI increased connection time, indicating that denser stands slowed or prevented connectivity. Linear mixed-effects models demonstrated that internet speed primarily declined with increasing distance, with download and upload rates dropping beyond 40 m from the router. LAI, RI, and VC did not influence connection time or speed, suggesting that overall stand density rather than leaf area per unit ground area has a greater impact on signal obstruction. Overall, dense forest structure and distance are the main constraints on LEO satellite connectivity and performance, and understanding these limitations supports the development and deployment of satellite-based networking to advance smart forestry operations. These results provide one of the first quantitative assessments of LEO satellite connectivity constraints in operational forest conditions, offering practical guidance for deploying satellite-based networks to support smart forestry applications in remote environments. Full article

Unmanned aerial vehicles (UAVs), a key enabler for the Internet of Things’ (IoT) evolution to 3D spatial dimensions, play a critical role in data collection across fields. However, path planning in obstacle-rich and threat-prone environments remains a core bottleneck for their safe and efficient operation. Traditional meta-heuristic algorithms suffer from insufficient exploration, slow convergence, and local optima issues. To address this, we propose an enhanced multi-mechanism DBO algorithm (MMDBO), integrating SPM chaotic mapping, dynamic global exploration, adaptive T-distribution, and dynamic weight mechanisms. Comparative experiments against five classical algorithms on 12 benchmarks test functions and three complex terrains show MMDBO achieves superior performance across the majority of key path-planning metrics—including flight trajectory length, altitude profile fidelity, and path smoothness—while incurring only a modest increase in computational time. The results of the statistical test further indicate that the MMDBO algorithm significantly outperforms the comparison algorithms in both convergence speed and accuracy. These advances deliver actionable, highly reliable guidance for UAV flight path optimization. Full article

Fifth-generation (5G) and sixth-generation (6G) wireless communications aim to achieve significantly higher data speeds, remarkably low latency, and substantial improvements in the efficiency of base stations. With the rapid increase in the utilization of broadband data driven by Internet of Things (IoT) gadgets, smart home systems, autonomous vehicles, and virtual reality devices, 5G and 6G networks are set to overcome the limitations of earlier telecommunication technologies and serve as key enablers for future IoT applications. Anticipated as the primary infrastructure for delivering emerging services, 5G cellular networks introduce new requirements and challenges that complicate the achievement of desired objectives. This paper provides a comprehensive overview of machine learning (ML) methods and their application in 5G and 6G wireless networks, covering supervised, unsupervised, and reinforcement learning (RL) approaches. ML is set to play a central and important role in 6G systems for these wireless networks. Subsequently, this paper thoroughly explores a series of challenges within the domain of 5G and 6G networks and examines research opportunities for applying ML techniques to address these challenges. Full article

The proliferation of Internet of Things (IoT) devices and services creates not only significant benefits but also new security threats. Classical information encryption techniques are not suitable for resource-constrained edge modules, thereby generating the demand for lightweight and efficient data protection algorithms. This work presents a novel dynamical parameter estimation scheme for chaotic oscillators, applied to physical-layer authentication (PLA). The proposed approach relies on the receiver’s capability to estimate a selected parameter of the transmitter’s oscillator determined by circuit configuration from the received chaotic signal using a locally synchronized oscillator, thereby enabling secure authentication based on a hardware-encoded identifier. The scheme is intended to complement a chaos-based wireless sensor network (WSN) architecture, where sensor nodes (SNs) implement analog chaotic oscillators, and the gateway operates discrete-time models. The Vilnius chaotic oscillator was chosen to validate the proposed PLA scheme. A rigorous bifurcation analysis of analytical, SPICE and discrete oscillator models was first conducted to identify parameter regions that preserve chaotic dynamics, establishing correspondence between models to guarantee the feasibility of parameter estimation across implementations. The digital realization of the parameter estimator demonstrated accurate and stable operation, with a small and nearly constant estimation relative error not exceeding 1.01%. Key performance metrics were analyzed, including estimation time, precision, and noise robustness. A tradeoff between estimation speed and accuracy was identified, particularly under noisy channel conditions. Finally, the influence of the receiver’s native oscillator parameter on distinguishable transmitter parameter ranges was demonstrated, highlighting the configurability and security potential of the proposed system against unauthorized transmissions. Full article

Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage. Full article

Migrating to Post-Quantum Cryptography (PQC) is critical for securing resource-constrained Internet of Things (IoT) devices against the “harvest-now, decrypt-later” threat. While ML-KEM (CRYSTALS-Kyber) has been standardized under FIPS 203 for general encryption, these devices often operate on unreliable networks suffering from high latency and packet loss. Our recent systematic review identified a critical gap that existing research overwhelmingly focuses on Transport Layer Security (TLS). This leaves the resilience of lightweight protocols like MQTT and CoAP under challenging network conditions largely unexplored. This paper introduces PQC-IoTNet, a novel Software-in-the-Loop (SITL) framework to address this gap. Our three-tier architecture integrates a Python-based IoT client with kernel-level emulation to test the full protocol stack. Validation results comparing Kyber and ECC demonstrate the framework’s ability to capture critical performance cliffs caused by TCP retransmissions. Notably, the framework revealed that while Kyber maintained an 18% speed advantage over ECC at 5% packet loss, both protocols experienced nonlinear latency spikes. This work provides a reproducible blueprint to identify operational boundaries and select resilient protocols for secure IoT systems. Full article

Wireless communication systems, which rely on radio frequencies (RFs), are widely utilized in various applications, such as mobile communications, radio frequency identification, marine networks, smart farms, and smart homes. Due to their ease of installation, wireless systems offer advantages over wired alternatives. But the deployment of high-frequency radio waves for a communication system can pose potential health risks. To address these concerns, many researchers have explored the use of visible light as a safer alternative to radio frequency communication. In this context, optical camera communication has emerged as a good candidate compared to the RF system. Meanwhile, artificial intelligence (AI) is reshaping industries and human life by solving complex problems, enabling intelligent automation, and driving advancements in technologies such as smart farms, smart homes, and future internet of things systems. In this study, we recommend a Multiple-Input Multiple-Output Camera On–Off Keying (MIMO C-OOK) modulation that integrates a YOLOv11 for light source detection and tracking and a deep learning network-based decoder algorithm, optimized for long-range and mobility communication scenarios. The proposed approach enhances the conventional C-OOK system by increasing the data rate and transmission range while reducing errors at the receiver. Implementation results show that the proposed approach can achieve reliable communication up to 10 m with minimal errors, even under mobility conditions (3 m/s, equivalent to walking speed), by optimizing camera parameters and employing forward error correction (FEC). Full article